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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Shuttling Majorana zero modes in disordered and noisy topological superconductors

The braiding of Majorana zero modes (MZMs) forms the fundamental building block for topological quantum computation. Braiding protocols which involve the physical exchange of MZMs are typically envisioned on a network of topological superconducting wires. An important component of these protocols is the transport of MZMs, which can be performed by using electric gates to locally tune sections of the wire between topologically trivial and nontrivial phases. In this work, we numerically simulate this transport by tuning a single section of a superconducting wire which contains either disorder (uncorrelated and correlated) or noise. We focus on the impact of these additional effects on the diabatic error, which describes unwanted transitions between the ground state and excited states. We show that the behavior of the average diabatic error is predominantly controlled by the statistics of the minimum bulk energy gap which is suppressed in the presence of disorder. The increase in diabatic error can be several orders of magnitude and is most deleterious when the disorder correlation length is a finite fraction of the transport distance and negligible when these lengths are far apart. In the presence of noise, the diabatic error is significantly enhanced due to optical transitions which depend on the minimum bulk energy gap as well as the frequency modes present in the noise. The results presented here serve to further characterize the diabatic error in disordered and noisy settings, which are important considerations in practical implementations of physical braiding schemes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Perspectives on the integration between first-principles and data-driven modeling

Efficiently embedding and/or integrating mechanistic information with data-driven models is essential if it is desired to simultaneously take advantage of both engineering principles and data-science. Further the opportunity for hybridization occurs in many scenarios, such as the development of a faster model of an accurate high-fidelity computer model; the correction of a mechanistic model that does not fully-capture the physical phenomena of the system; or the integration of a data-driven component approximating an unknown correlation within a mechanistic model. At the same time, different techniques have been proposed and applied in different literatures to achieve this hybridization, such as hybrid modeling, physics-informed Machine Learning (ML) and model calibration. In this paper we review the methods, challenges, applications and algorithms of these three research areas and discuss them in the context of the different hybridization scenarios. Moreover, we provide a comprehensive comparison of the hybridization techniques with respect to their differences and similarities, as well as advantages and limitations and future perspectives. Finally, we apply and illustrate hybrid modeling, physics-informed ML and model calibration via a chemical reactor case study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Benchmark of the KGMf with a coupled Boltzmann equation solver

The Kinetic Global Model framework (KGMf) is an open-source general-purpose global model (spatially averaged) simulation code developed to explore the reaction kinetics and pathways in plasma discharge systems. It contains species continuity and electron energy balance equations, with a time-dependent evaluated electron energy distribution function (EEDF) for electron impact reactions. The EEDF is utilized to determine the rate coefficients for electron impact reactions, which can have profound impact on the temporal evolutions of plasma parameters. Previously, the EEDF was commonly assumed as Maxwellian or an analytical function of the effective electron temperature. In this work, the KGMf is coupled with a Boltzmann equation (BE) solver to self-consistently compute the EEDF. The EEDF evolution frequency is determined based on relative changes of the reduced electric field. The KGMf is benchmarked with the ZDPlasKin code based on high-pressure low-temperature argon plasma discharge cases. Additionally, the temporal evolutions of reduced electric field, electron temperature, EEDF, reaction rates, and species densities, are obtained and compared under different discharge conditions, showing good agreement between the KGMf and the ZDPlasKin simulations. The application of the KGMf for predicting breakdown times in high power microwave discharges is also presented, which shows qualitative agreement with particle-in-cell simulations. The KGMf can be further applied for more complicated plasma discharge systems, where the reaction kinetics are more intricate (e.g., plasma-assisted combustion systems).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

New Dimension in Ab Initio Electronic Structure Theory: Temperature, Pressure, and Chemical Potential

Ab initio electronic structure theory has transformed gas-phase molecular science with its predictive ability. In the attempt to bring such predictive ability to macroscopic systems and condensed matter, the theory must integrate quantum mechanics with statistical thermodynamics, so that thermodynamic functions such as free energy, internal energy, entropy, and chemical potentials are computed as functions of temperature in a systematically converging series of approximations. Here, a general, versatile strategy of elevating ab initio electronic structure theory to nonzero temperatures is introduced and discussed.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Approximation of nearly-periodic symplectic maps via structure-preserving neural networks

A continuous-time dynamical system with parameter ε is nearly-periodic if all its trajectories are periodic with nowhere-vanishing angular frequency as ε approaches 0. Nearly-periodic maps are discrete-time analogues of nearly-periodic systems, defined as parameter-dependent diffeomorphisms that limit to rotations along a circle action, and they admit formal U(1) symmetries to all orders when the limiting rotation is non-resonant. For Hamiltonian nearly-periodic maps on exact presymplectic manifolds, the formal U(1) symmetry gives rise to a discrete-time adiabatic invariant. In this paper, we construct a novel structure-preserving neural network to approximate nearly-periodic symplectic maps. This neural network architecture, which we call symplectic gyroceptron, ensures that the resulting surrogate map is nearly-periodic and symplectic, and that it gives rise to a discrete-time adiabatic invariant and a long-time stability. This new structure-preserving neural network provides a promising architecture for surrogate modeling of non-dissipative dynamical systems that automatically steps over short timescales without introducing spurious instabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

The new bern PET cyclotron, its research beam line, and the development of an innovative beam monitor detector

The new Bern cyclotron laboratory aims at industrial radioisotope production for PET diagnostics and multidisciplinary research by means of a specifically conceived beam transfer line, terminated in a separate bunker. In this framework, an innovative beam monitor detector based on doped silica and optical fibres has been designed, constructed, and tested. Scintillation light produced by Ce and Sb doped silica fibres moving across the beam is measured, giving information on beam position, shape, and intensity. The doped fibres are coupled to commercial optical fibres, allowing the read-out of the signal far away from the radiation source. This general-purpose device can be easily adapted for any accelerator used in medical applications and is suitable either for low currents used in hadrontherapy or for currents up to a few {mu}A for radioisotope production, as well as for both pulsed and continuous beams.

43 PARTICLE ACCELERATORS↗
DOE OSTI2014

Compact focusing spectrometer: Visible (1 eV) to hard x-rays (200 keV)

A low-cost spectrometer that covers a wide range of photon energies can be useful to teach spectroscopy, and for simple, rapid measurements of the photon spectrum produced by small plasma devices. The spectrometer here achieves its wide range, nominally from 1 eV to 200 keV, with a series of spherically and cylindrically bent gratings or crystals that all have the same shape and the same radius of curvature; they are complemented by matching apertures and diagnostics on the Rowland circle that serves as the circular part of the spectrometer's vacuum vessel. Spectral lines are easily identified with software that finds their positions from the dispersion of each diffractive element and the known energies of the lines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Solving the electronic structure problem for over 100000 atoms in real space

Using a real-space high-order finite-difference approach, we investigate the electronic structure of large spherical silicon nanoclusters. Within Kohn-Sham density functional theory and using pseudopotentials, we report the self-consistent field convergence of a system with over 100000 atoms: a Si 107,641 ⁢H 9,084 nanocluster with a diameter of 16 nm. Our approach uses Chebyshev-filtered subspace iteration to speed up the convergence of the eigenspace, and blockwise Hilbert space-filling curves to speed up sparse matrix-vector multiplications, all of which are implemented in the parsec code. For the largest system, we utilized 2048 nodes (114 688 cores) on the Frontera machine in the Texas Advanced Computing Center. Our quantitative analysis of the electronic structure shows how it gradually approaches its bulk counterpart as a function of nanocluster size. The band gap is enlarged due to quantum confinement in nanoclusters, but decreases as the system size increases, as expected. In conclusion, our work serves as a proof of concept for the capacity of the real-space approach in efficiently parallelizing very large calculations using high-performance computer platforms, which can straightforwardly be replicated in other systems with more than 10 5 atoms.

0-dimensional systems↗
DOE OSTI2016

Hybrid Enrichment Verification Array: Module Characterization Studies

The work presented in this report is focused on the characterization and refinement of the Hybrid Enrichment Verification Array (HEVA) approach, which combines the traditional 186-keV 235 U signature with high-energy prompt gamma rays from neutron capture in the detector and surrounding collimator material, to determine the relative enrichment and 235 U mass of the cylinder. The design of the HEVA modules (hardware and software) deployed in the current field trial builds on over seven years of study and evolution by PNNL, and consists of a ø3''×3'' NaI(Tl) scintillator coupled to an Osprey digital multi-channel analyzer tube base from Canberra. The core of the HEVA methodology, the high-energy prompt gamma-ray signature, serves as an indirect method for the measurement of total neutron emission from the cylinder. A method for measuring the intrinsic efficiency of this “non-traditional” neutron signature and the results from a benchmark experiment are presented. Also discussed are potential perturbing effects on the non-traditional signature, including short-lived activation of materials in the HEVA module. Modeling and empirical results are presented to demonstrate that such effects are expected to be negligible for the envisioned implementation scenario. In comparison to previous versions, the new design boosts the high-energy prompt gamma-ray signature, provides more flexible and effective collimation, and improves count-rate management via commercially available pulse-processing electronics with a special modification prompted by PNNL.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

In October 2021, the U.S. Department of Energy (DOE) welcomed participants to the Artificial Intelligence for Earth System Predictability (AI4ESP) Workshop, hosted by the Office of Biological and Environmental Research (BER)—Advanced Scientific Computing Research (ASCR). The workshop is part of BER-ASCR’s ambition to more radically and aggressively advance prediction capabilities in the climate, Earth, and environmental sciences through the use of modern data analytics and artificial intelligence (AI). Advances in these capabilities are needed to improve predictions of climate change and extreme events that provide actionable information for planning and building resilience to their impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Solar Fuels Nexus: Molecules and Materials for Light-Driven Catalysis

The American Chemical Society (ACS) selects two groups of graduate students each year to plan and host a one-day symposium at each national meeting (both fall and spring).This year our Graduate Student Symposium Planning Committee (GSSPC), composed of seven students from four universities, proposal entitled “A Solar Fuels Nexus: Molecules and Materials for Light-Driven Catalysis” was selected for the “Crossroads in Chemistry” ACS Meeting that will take place March 23-26, 2023 in Indianapolis, IN. All members of our GSSPC are affiliated with the DOE Fuels from Sunlight Energy Innovation Hub, with two from the Liquid Sunlight Alliance (LiSA) and five from the Center for Hybrid Approaches in Solar Energy to Liquid Fuels (CHASE). Here we request funds to support this symposium. This symposium will highlight research progress and perspectives in the solar fuels generation field and seeks to advance the four priority research objectives (PROs) established by the Department of Energy’s Office of Basic Energy Sciences (DOE-BES) Roundtable Report that are also central to many research goals within LiSA and CHASE. The symposium will consist of research presentations from 10 invited senior researcher speakers on topics such as molecular catalyst design, computational modeling of electron transfer systems, microenvironmental effects on CO2 reduction and H2O oxidation catalysis, and intelligent design of semiconductor interfaces with ample time for discussions. These research topics fit very well with the Solar Photochemistry supported research areas of “light-driven electron and energy transfer in condensed phase and interfacial molecular systems,” “electrocatalysis and photocatalysis of solar fuels reactions,” and “semiconductor photoelectrochemistry.” More broadly, this symposium seeks to advance the DOE-BES’s mission to: “support fundamental research to understand, predict, and ultimately control matter and energy at the level of electrons, atoms, and molecules” by providing a diverse atmosphere where such research will be disseminated, discussed, and debated. There will be a strong focus on Diversity, Equity, and Inclusivity (DEI) in our symposium. Of our 10 speakers, 7 will be from underrepresented demographics in STEM, including 5 who identify as women. Furthermore, we have representatives from academia accompanied by one national lab scientist and one officer from the Office of Fossil Energy and Carbon Management at the DOE. All speakers will be holding a short DEI moment ahead of their talks. In order to support the career development of attending early career scientists, we will also be hosting a luncheon specifically for graduate students and postdocs to provide them opportunities to network with the distinguished speakers and other attendees. DOE funds for this symposium will be used to support the attendance and participation of 15 graduate students from US institutions by defraying travel and registration costs. These funds will promote engagement and conversation between early career scientists in the solar fuels field, while disseminating solar fuels research funded by and relevant to the DOE.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1995

Data handling and visualization for NASA's science programs

Advanced information systems capabilities are essential to conducting NASA's scientific research mission. Access to these capabilities is no longer a luxury for a select few within the science community, but rather an absolute necessity for carrying out scientific investigations. The dependence on high performance computing and networking, as well as ready and expedient access to science data, metadata, and analysis tools is the fundamental underpinning for the entire research endeavor. At the same time, advances in the whole range of information technologies continues on an almost explosive growth path, reaching beyond the research community to affect the population as a whole. Capitalizing on and exploiting these advances are critical to the continued success of space science investigations. NASA must remain abreast of developments in the field and strike an appropriate balance between being a smart buyer and a direct investor in the technology which serves its unique requirements. Another key theme deals with the need for the space and computer science communities to collaborate as partners to more fully realize the potential of information technology in the space science research environment.

Bredekamp, Joseph H.↗
NASA NTRS1997

Center of Excellence in Space Data and Information Science, Year 9

This report summarizes the range of computer science related activities undertaken by CESDIS(Center of Excellence in Space Data and Information Sciences) for NASA in the twelve months from July 1, 1996 through June 30, 1997. These activities address issues related to accessing, processing, and analyzing data from space observing systems through collaborative efforts with university, industry, and NASA space and Earth scientists.

DATA MANAGEMENT↗
NASA NTRS2006

Biology Inspired Approach for Communal Behavior in Sensor Networks

Research in wireless sensor network technology has exploded in the last decade. Promises of complex and ubiquitous control of the physical environment by these networks open avenues for new kinds of science and business. Due to the small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors working in concert. Although the reduction in size has been phenomenal it results in severe limitations on the computing, communicating, and power capabilities of these devices. Under these constraints, research efforts have concentrated on developing techniques for performing relatively simple tasks with minimal energy expense assuming some form of centralized control. Unfortunately, centralized control does not scale to massive size networks and execution of simple tasks in sparsely populated networks will not lead to the sophisticated applications predicted. These must be enabled by new techniques dependent on local and autonomous cooperation between sensors to effect global functions. As a step in that direction, in this work we detail a technique whereby a large population of sensors can attain a global goal using only local information and by making only local decisions without any form of centralized control.

Jones, Kennie H.↗
DOE OSTI2020

HPC at NREL: FY 2019

Brochure featuring Fiscal Year 2019 accomplishments at the NREL High-Performance Computing User Facility and Data Center at the Energy Systems Integration Facility, including information on the facility's HPC systems as well as highlights from several modeling and simulation projects across the spectrum of energy efficiency and renewable energy technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Materials informatics: From the atomic-level to the continuum

We report that in recent years materials informatics, which is the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. This relatively new field is already having a significant impact on the interpretation of data for a variety of materials systems, including those used in thermoelectrics, ferroelectrics, battery anodes and cathodes, hydrogen storage materials, polymer dielectrics, etc. Its practitioners employ the methods of multivariate statistics and machine learning in conjunction with standard computational tools (e.g., density-functional theory) to, for example, visualize and dimensionally reduce large data sets, identify patterns in hyperspectral data, parse microstructural images of polycrystals, characterize vortex structures in ferroelectrics, design batteries and, in general, establish correlations to extract important physics and infer structure-property-processing relationships. In this Overview, we critically examine the role of informatics in several important materials subfields, highlighting significant contributions to date and identifying known shortcomings. We specifically focus attention on the difference between the correlative approach of classical data science and the causative approach of physical sciences. From this perspective, we also outline some potential opportunities and challenges for informatics in the materials realm in this era of big data.

36 MATERIALS SCIENCE↗

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